# Applied Compute

**Canonical:** https://apis.io/providers/applied-compute/  
**Website:** https://www.appliedcompute.com  
**APIs profiled:** 0

Applied Compute is a San Francisco AI company founded in 2025 by former OpenAI researchers, building "Specific Intelligence" for the enterprise. It operates a cloud platform for training, inference, and continuous improvement of custom, open AI models and long-horizon, tool-using agents grounded in a company's own proprietary data. Rather than selling a generic model, Applied Compute embeds engineers with customer teams to fine-tune models, mine production data, run online/reinforcement learning, and deploy autonomous agents that operate inside the customer's environment with observability over rollouts. The company emerged from stealth in October 2025 and is backed by Kleiner Perkins, Benchmark, Sequoia Capital, Lux Capital, Greenoaks, Neo, Elad Gil, and others at a reported ~$1.3B valuation. Named customers include DoorDash, Cognition, Harvey, and Mercor.

## Kin Score — 13.8 / 100 (emerging)

Scored 2026-08-17 under rubric 0.11.0. Trend: flat (+0.0 from 13.8).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Operational Transparency | 5.3 |
| Developer Ergonomics | 19.6 |
| Commercial Clarity | 21.1 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Security (1)

- **Applied Compute Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Artificial Intelligence, Machine Learning, LLM, Model Training, Inference, AI Agents, Enterprise AI, Fine-Tuning, Reinforcement Learning

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/applied-compute/). Scores are computed from the provider's own public artifacts under a published rubric.